Predicting COVID-19 and Influenza Vaccination Confidence and Uptake in the United States

Lijiang Shen, Daniel Lee

Research output: Contribution to journalArticlepeer-review

Abstract

This study investigates and compares the predictors of COVID-19 and influenza vaccination confidence and uptake in the U.S. Vaccine hesitancy is defined as the reluctance or refusal (i.e., less than 100% behavioral intention) to vaccinate despite the availability of effective and safe vaccines. Vaccine hesitancy is a major obstacle in the fight against infectious diseases such as COVID-19 and influenza. Predictors of vaccination intention are identified using the reasoned action approach and the integrated behavioral model. Data from two national samples (N = 1131 for COVID-19 and N = 1126 for influenza) were collected from U.S. Qualtrics panels. Tobit regression models were estimated to predict percentage increases in vaccination intention (i.e., confidence) and the probability of vaccination uptake (i.e., intention reaching 100%). The results provided evidence for the reasoned approach and the IBM model and showed that the predictors followed different patterns for COVID-19 and influenza. The implications for intervention strategies and message designs were discussed.

Original languageEnglish (US)
Article number1597
JournalVaccines
Volume11
Issue number10
DOIs
StatePublished - Oct 2023

All Science Journal Classification (ASJC) codes

  • Immunology
  • Pharmacology
  • Drug Discovery
  • Infectious Diseases
  • Pharmacology (medical)

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